knitr::opts_chunk$set(echo = TRUE)
library(tidyverse) # great collection of packages for data carpentry, modelling, and visualization
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.0.6 ✓ dplyr 1.0.4
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(readr)
library(haven) # package for loading Stata's .dta files.
library(sjlabelled) # good for renaming, changing classes, etc. in the piped dplyr mode
##
## Attaching package: 'sjlabelled'
## The following objects are masked from 'package:haven':
##
## as_factor, read_sas, read_spss, read_stata, write_sas, zap_labels
## The following object is masked from 'package:forcats':
##
## as_factor
## The following object is masked from 'package:dplyr':
##
## as_label
library(zscorer)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(anthro)
library(childsds)
library(sjlabelled)
library(sjPlot)
library(VIF)
library(car)
## Loading required package: carData
## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4
##
## Attaching package: 'car'
## The following object is masked from 'package:VIF':
##
## vif
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
# install.packages("interactions")
library(interactions)
growth_clocks_data <- read_csv(here::here ("Data/growth_clocks_data.csv"))
## Warning: Missing column names filled in: 'X1' [1]
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## basebrgy_basewman = col_character(),
## birthdate = col_date(format = ""),
## intwdate_birth = col_date(format = ""),
## date_inf_meas = col_date(format = ""),
## intwdate91 = col_date(format = ""),
## intw_date02 = col_date(format = ""),
## reprostat = col_character(),
## was_preg_no_na = col_character(),
## trimester = col_character(),
## SampleID = col_character(),
## Comment = col_character(),
## predictedGender = col_character(),
## predictedTissue = col_character(),
## Tissue = col_character()
## )
## ℹ Use `spec()` for the full column specifications.
growth_clocks_data
## # A tibble: 3,023 x 158
## X1 uncchdid basebrgy_basewm… sexchild sex outcome birthdate
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <date>
## 1 1 20001 1_12 1 NA 1 1983-06-03
## 2 2 20002 1_14 2 NA 1 1983-05-23
## 3 3 20003 1_21 2 NA 1 1983-05-13
## 4 4 20004 1_23 1 1 1 1983-05-19
## 5 5 20006 1_26 2 NA 1 1983-06-20
## 6 6 20007 1_27 2 2 1 1983-05-16
## 7 7 20008 1_30 1 1 1 1983-05-03
## 8 8 20009 1_32 2 NA 1 1983-05-20
## 9 9 20010 1_33 2 2 1 1983-05-07
## 10 10 20011 1_34 2 NA 1 1983-05-31
## # … with 3,013 more rows, and 151 more variables: intwdate_birth <date>,
## # age_days_birthweigh <dbl>, weightak_birth_kg <dbl>, heightcm_birth <dbl>,
## # date_inf_meas <date>, age_days_infweigh <dbl>, hght_12 <dbl>,
## # wght_12 <dbl>, intwdate91 <date>, age91_days <dbl>, age_mo_91 <dbl>,
## # age91_years <dbl>, hightndx_91 <dbl>, weghtndx_91 <dbl>,
## # intw_date02 <date>, age_days_02 <dbl>, age_years_02 <dbl>,
## # age_cutoff_02 <dbl>, age_cutoff_02_days <dbl>, height_02 <dbl>,
## # weight_02 <dbl>, age_05 <dbl>, height_05 <dbl>, height_m_05 <dbl>,
## # weight_05 <dbl>, bmi_05 <dbl>, hfaz_birth <dbl>, hfaz_inf_12 <dbl>,
## # hfaz_91 <dbl>, hfaz_02 <dbl>, hfa_diff_birth_inf12 <dbl>,
## # hfa_diff_inf12_91 <dbl>, hfa_diff_91_02 <dbl>, wfaz_birth <dbl>,
## # wfaz_inf_12 <dbl>, wfaz_91 <dbl>, wfa_diff_birth_inf12 <dbl>,
## # wfa_diff_inf12_91 <dbl>, reprostat <chr>, was_preg_no_na <chr>,
## # trimester <chr>, SampleID <chr>, DNAmAge <dbl>, Comment <chr>,
## # noMissingPerSample <dbl>, meanMethBySample <dbl>, minMethBySample <dbl>,
## # maxMethBySample <dbl>, corSampleVSgoldstandard <dbl>,
## # meanAbsDifferenceSampleVSgoldstandard <dbl>, predictedGender <chr>,
## # meanXchromosome <dbl>, predictedTissue <chr>,
## # ProbabilityFrom.X.Vasc.Endoth.Umbilical. <dbl>,
## # ProbabilityFrom.Ape.WB <dbl>, ProbabilityFrom.Blood.CD4.Tcells <dbl>,
## # ProbabilityFrom.Blood.CD4.CD14 <dbl>,
## # ProbabilityFrom.Blood.Cell.Types <dbl>, ProbabilityFrom.Blood.Cord <dbl>,
## # ProbabilityFrom.Blood.PBMC <dbl>, ProbabilityFrom.Blood.WB <dbl>,
## # ProbabilityFrom.Bone <dbl>, ProbabilityFrom.Brain.Cerebellar <dbl>,
## # ProbabilityFrom.Brain.CRBLM <dbl>, ProbabilityFrom.Brain.FCTX <dbl>,
## # ProbabilityFrom.Brain.Occipital.Cortex <dbl>,
## # ProbabilityFrom.Brain.PONS <dbl>, ProbabilityFrom.Brain.Prefr.CTX <dbl>,
## # ProbabilityFrom.Brain.TCTX <dbl>, ProbabilityFrom.Breast <dbl>,
## # ProbabilityFrom.Breast.NL <dbl>, ProbabilityFrom.Buccal <dbl>,
## # ProbabilityFrom.Cartilage.Knee <dbl>, ProbabilityFrom.Colon <dbl>,
## # ProbabilityFrom.Dermal.fibroblast <dbl>, ProbabilityFrom.Epidermis <dbl>,
## # ProbabilityFrom.Fat.Adip <dbl>, ProbabilityFrom.Gastric <dbl>,
## # ProbabilityFrom.GlialCell <dbl>, ProbabilityFrom.Head.Neck <dbl>,
## # ProbabilityFrom.Heart <dbl>, ProbabilityFrom.Kidney <dbl>,
## # ProbabilityFrom.Liver <dbl>, ProbabilityFrom.Liver. <dbl>,
## # ProbabilityFrom.Lung <dbl>, ProbabilityFrom.MSC <dbl>,
## # ProbabilityFrom.Muscle <dbl>, ProbabilityFrom.Neuron <dbl>,
## # ProbabilityFrom.Placenta <dbl>, ProbabilityFrom.Prostate.NL <dbl>,
## # ProbabilityFrom.Saliva <dbl>, ProbabilityFrom.Sperm <dbl>,
## # ProbabilityFrom.Stomach <dbl>, ProbabilityFrom.Thyroid <dbl>,
## # ProbabilityFrom.Uterine.Cervix <dbl>,
## # ProbabilityFrom.Uterine.Endomet <dbl>, AgeAccelerationDiff <dbl>,
## # AgeAccelerationResidual <dbl>, Female <dbl>, Age <dbl>, …
Birth to 2 years old
grim_height_b_2_f<-lm(AgeAccelGrim ~ hfa_diff_birth_inf12 +
was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_height_b_2_m<-lm(AgeAccelGrim ~ hfa_diff_birth_inf12, subset(growth_clocks_data, sex == "1"))
pheno_height_b_2_f <-update(grim_height_b_2_f, AgeAccelPheno ~ .)
pheno_height_b_2_m <-update(grim_height_b_2_m, AgeAccelPheno ~ .-was_preg_no_na)
han_height_b_2_f <-update(grim_height_b_2_f, EEAA ~ .)
han_height_b_2_m <-update(grim_height_b_2_m, EEAA ~ . -was_preg_no_na)
horv_height_b_2_f <-update(grim_height_b_2_f, IEAA ~ .)
horv_height_b_2_m <-update(grim_height_b_2_m, IEAA ~ . -was_preg_no_na)
sjPlot::tab_model(grim_height_b_2_f, grim_height_b_2_m,
pheno_height_b_2_f, pheno_height_b_2_m,
han_height_b_2_f, han_height_b_2_m,
horv_height_b_2_f, horv_height_b_2_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-0.93
|
-1.39 – -0.48
|
<0.001
|
1.80
|
0.76 – 2.84
|
0.001
|
-0.34
|
-1.29 – 0.61
|
0.478
|
-2.19
|
-4.02 – -0.37
|
0.019
|
-0.39
|
-1.39 – 0.61
|
0.442
|
0.33
|
-1.49 – 2.16
|
0.719
|
0.09
|
-0.50 – 0.68
|
0.757
|
1.15
|
-0.16 – 2.47
|
0.085
|
|
hfa_diff_birth_inf12
|
-0.04
|
-0.23 – 0.16
|
0.717
|
0.16
|
-0.30 – 0.63
|
0.483
|
-0.04
|
-0.43 – 0.36
|
0.859
|
-0.51
|
-1.33 – 0.30
|
0.214
|
0.01
|
-0.41 – 0.43
|
0.962
|
-0.35
|
-1.17 – 0.47
|
0.397
|
0.09
|
-0.16 – 0.34
|
0.473
|
0.54
|
-0.04 – 1.13
|
0.070
|
|
was_preg_no_na [Yes]
|
2.79
|
2.20 – 3.39
|
<0.001
|
|
|
|
3.47
|
2.23 – 4.71
|
<0.001
|
|
|
|
0.96
|
-0.34 – 2.26
|
0.146
|
|
|
|
-0.00
|
-0.77 – 0.77
|
0.995
|
|
|
|
|
Observations
|
372
|
100
|
372
|
100
|
372
|
100
|
372
|
100
|
|
R2 / R2 adjusted
|
0.189 / 0.185
|
0.005 / -0.005
|
0.076 / 0.071
|
0.016 / 0.006
|
0.006 / 0.000
|
0.007 / -0.003
|
0.001 / -0.004
|
0.033 / 0.023
|
growth_clocks_data %>%
select(uncchdid, hfa_diff_birth_inf12, AgeAccelGrim, AgeAccelPheno, EEAA, IEAA) %>%
gather(key = clock_type, value = AgeAccel, -c(1,2)) %>%
na.omit() %>%
ggplot(., aes(x = hfa_diff_birth_inf12, y = AgeAccel, col = clock_type))+
geom_point()+
scale_color_brewer(type = "qual", palette = 6)+
facet_wrap(~clock_type)+
theme(legend.position = "none")

#birth-2 yrs old visualization
par(mfrow=c(2,2))
plot(grim_height_b_2_f)

par(mfrow=c(2,2))
plot(grim_height_b_2_m)

par(mfrow=c(2,2))
plot(pheno_height_b_2_f)

par(mfrow=c(2,2))
plot(pheno_height_b_2_m)

par(mfrow=c(2,2))
plot(han_height_b_2_f)

par(mfrow=c(2,2))
plot(han_height_b_2_m)

#hfaz 2years old model
grim_height_83_91_f <-lm(AgeAccelGrim ~ hfa_diff_inf12_91 +
hfa_diff_birth_inf12 +
was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_height_83_91_m<-lm(AgeAccelGrim ~ hfa_diff_inf12_91 +
hfa_diff_birth_inf12, subset(growth_clocks_data, sex == "1"))
pheno_height_83_91_f <-update(grim_height_83_91_f, AgeAccelPheno ~ .)
pheno_height_83_91_m <-update(grim_height_83_91_m, AgeAccelPheno ~ .-was_preg_no_na)
han_height_83_91_f <-update(grim_height_83_91_f, EEAA ~ .)
han_height_83_91_m <-update(grim_height_83_91_m, EEAA ~ .-was_preg_no_na)
horv_height_83_91_f <-update(grim_height_83_91_f, IEAA ~ .)
horv_height_83_91_m <-update(grim_height_83_91_m, IEAA ~ .-was_preg_no_na)
sjPlot::tab_model(grim_height_83_91_f, grim_height_83_91_m,
pheno_height_83_91_f, pheno_height_83_91_m,
han_height_83_91_f, han_height_83_91_m,
horv_height_83_91_f, horv_height_83_91_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-0.89
|
-1.35 – -0.43
|
<0.001
|
1.96
|
0.93 – 2.98
|
<0.001
|
-0.24
|
-1.20 – 0.71
|
0.617
|
-1.86
|
-3.64 – -0.09
|
0.040
|
-0.21
|
-1.21 – 0.78
|
0.672
|
0.71
|
-1.05 – 2.46
|
0.426
|
0.07
|
-0.53 – 0.66
|
0.820
|
1.28
|
-0.04 – 2.60
|
0.056
|
|
hfa_diff_inf12_91
|
0.16
|
-0.12 – 0.43
|
0.268
|
0.92
|
0.16 – 1.68
|
0.018
|
0.18
|
-0.40 – 0.76
|
0.542
|
1.93
|
0.61 – 3.24
|
0.004
|
0.52
|
-0.09 – 1.12
|
0.093
|
2.19
|
0.89 – 3.48
|
0.001
|
-0.31
|
-0.67 – 0.05
|
0.094
|
0.75
|
-0.23 – 1.73
|
0.131
|
|
hfa_diff_birth_inf12
|
0.01
|
-0.19 – 0.22
|
0.898
|
0.41
|
-0.08 – 0.91
|
0.103
|
0.03
|
-0.40 – 0.47
|
0.876
|
0.01
|
-0.86 – 0.87
|
0.989
|
0.18
|
-0.27 – 0.63
|
0.433
|
0.24
|
-0.61 – 1.09
|
0.576
|
0.01
|
-0.26 – 0.28
|
0.953
|
0.75
|
0.11 – 1.39
|
0.023
|
|
was_preg_no_na [Yes]
|
2.76
|
2.17 – 3.36
|
<0.001
|
|
|
|
3.42
|
2.18 – 4.66
|
<0.001
|
|
|
|
0.85
|
-0.44 – 2.14
|
0.196
|
|
|
|
0.02
|
-0.75 – 0.79
|
0.957
|
|
|
|
|
Observations
|
370
|
100
|
370
|
100
|
370
|
100
|
370
|
100
|
|
R2 / R2 adjusted
|
0.191 / 0.185
|
0.061 / 0.042
|
0.076 / 0.069
|
0.095 / 0.076
|
0.013 / 0.005
|
0.110 / 0.092
|
0.009 / 0.001
|
0.056 / 0.036
|
#83-91 visualization
par(mfrow=c(2,2))
plot(grim_height_83_91_f)

par(mfrow=c(2,2))
plot(grim_height_83_91_m)

par(mfrow=c(2,2))
plot(pheno_height_83_91_f)

par(mfrow=c(2,2))
plot(pheno_height_83_91_m)

par(mfrow=c(2,2))
plot(han_height_83_91_f)

par(mfrow=c(2,2))
plot(han_height_83_91_m)

8 to 19 years old
#hfaz_91 minimal models
grim_height_91_02_f<-lm(AgeAccelGrim ~ hfa_diff_91_02 +
hfa_diff_birth_inf12 +
was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_height_91_02_m<-lm(AgeAccelGrim ~ hfa_diff_91_02 +
hfa_diff_birth_inf12, subset(growth_clocks_data, sex == "1"))
pheno_height_91_02_f <-update(grim_height_91_02_f, AgeAccelPheno ~ .)
pheno_height_91_02_m <-update(grim_height_91_02_m, AgeAccelPheno ~ .-was_preg_no_na)
han_height_91_02_f <-update(grim_height_91_02_f, EEAA ~ .)
han_height_91_02_m <-update(grim_height_91_02_m, EEAA ~ .-was_preg_no_na)
horv_height_91_02_f <-update(grim_height_91_02_f, IEAA ~ .)
horv_height_91_02_m <-update(grim_height_91_02_m, IEAA ~ .-was_preg_no_na)
sjPlot::tab_model(grim_height_91_02_f, grim_height_91_02_m,
pheno_height_91_02_f, pheno_height_91_02_m,
han_height_91_02_f, han_height_91_02_m,
horv_height_91_02_f, horv_height_91_02_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-0.94
|
-1.39 – -0.48
|
<0.001
|
1.90
|
0.86 – 2.93
|
<0.001
|
-0.30
|
-1.26 – 0.66
|
0.538
|
-2.05
|
-3.87 – -0.22
|
0.029
|
-0.35
|
-1.35 – 0.65
|
0.493
|
0.33
|
-1.51 – 2.18
|
0.721
|
0.11
|
-0.47 – 0.70
|
0.703
|
1.21
|
-0.11 – 2.54
|
0.073
|
|
hfa_diff_91_02
|
-0.33
|
-0.68 – 0.03
|
0.069
|
-0.90
|
-1.97 – 0.16
|
0.096
|
-0.38
|
-1.13 – 0.36
|
0.310
|
-1.35
|
-3.22 – 0.53
|
0.158
|
-0.51
|
-1.29 – 0.26
|
0.194
|
-0.02
|
-1.91 – 1.88
|
0.987
|
0.28
|
-0.18 – 0.74
|
0.229
|
-0.54
|
-1.90 – 0.83
|
0.435
|
|
hfa_diff_birth_inf12
|
-0.07
|
-0.27 – 0.13
|
0.480
|
0.11
|
-0.36 – 0.57
|
0.644
|
-0.05
|
-0.46 – 0.36
|
0.820
|
-0.60
|
-1.42 – 0.22
|
0.151
|
-0.04
|
-0.47 – 0.38
|
0.840
|
-0.35
|
-1.18 – 0.48
|
0.403
|
0.15
|
-0.10 – 0.41
|
0.238
|
0.51
|
-0.08 – 1.11
|
0.092
|
|
was_preg_no_na [Yes]
|
2.72
|
2.12 – 3.32
|
<0.001
|
|
|
|
3.45
|
2.20 – 4.70
|
<0.001
|
|
|
|
0.87
|
-0.44 – 2.17
|
0.192
|
|
|
|
0.07
|
-0.70 – 0.84
|
0.859
|
|
|
|
|
Observations
|
364
|
100
|
364
|
100
|
364
|
100
|
364
|
100
|
|
R2 / R2 adjusted
|
0.193 / 0.187
|
0.033 / 0.013
|
0.080 / 0.073
|
0.036 / 0.016
|
0.010 / 0.002
|
0.007 / -0.013
|
0.006 / -0.002
|
0.039 / 0.019
|
#91-02 visualization
par(mfrow=c(2,2))
plot(grim_height_91_02_f)

par(mfrow=c(2,2))
plot(grim_height_91_02_m)

par(mfrow=c(2,2))
plot(pheno_height_91_02_f)

par(mfrow=c(2,2))
plot(pheno_height_91_02_m)

par(mfrow=c(2,2))
plot(han_height_91_02_f)

par(mfrow=c(2,2))
plot(han_height_91_02_m)

modeling wfaz (no interactions)
Birth to 2 years old
#wfa birth minimal models
grim_weight_b_2_f<-lm(AgeAccelGrim ~ wfa_diff_birth_inf12 + was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_weight_b_2_m<-lm(AgeAccelGrim ~ wfa_diff_birth_inf12, subset(growth_clocks_data, sex == "1"))
pheno_weight_b_2_f <-update(grim_weight_b_2_f, AgeAccelPheno ~ .)
pheno_weight_b_2_m <-update(grim_weight_b_2_m, AgeAccelPheno ~ .)
han_weight_b_2_f <-update(grim_weight_b_2_f, EEAA ~ .)
han_weight_b_2_m <-update(grim_weight_b_2_m, EEAA ~ .)
horv_weight_b_2_f <-update(grim_weight_b_2_f, IEAA ~ .)
horv_weight_b_2_m <-update(grim_weight_b_2_m, IEAA ~ .)
sjPlot::tab_model(grim_weight_b_2_f, grim_weight_b_2_m,
pheno_weight_b_2_f, pheno_weight_b_2_m,
han_weight_b_2_f, han_weight_b_2_m,
horv_weight_b_2_f, horv_weight_b_2_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-0.76
|
-1.46 – -0.07
|
0.031
|
1.10
|
-0.84 – 3.04
|
0.262
|
0.29
|
-1.17 – 1.74
|
0.698
|
0.32
|
-3.09 – 3.73
|
0.851
|
0.76
|
-0.76 – 2.28
|
0.327
|
1.90
|
-1.51 – 5.31
|
0.271
|
0.30
|
-0.61 – 1.20
|
0.521
|
-0.37
|
-2.86 – 2.12
|
0.771
|
|
wfa_diff_birth_inf12
|
-0.04
|
-0.30 – 0.22
|
0.770
|
0.14
|
-0.53 – 0.81
|
0.683
|
-0.22
|
-0.76 – 0.32
|
0.420
|
-0.56
|
-1.73 – 0.62
|
0.352
|
-0.46
|
-1.03 – 0.10
|
0.106
|
-0.33
|
-1.50 – 0.85
|
0.584
|
-0.15
|
-0.49 – 0.18
|
0.373
|
0.18
|
-0.68 – 1.04
|
0.684
|
|
was_preg_no_na [Yes]
|
2.80
|
2.21 – 3.40
|
<0.001
|
|
|
|
3.52
|
2.28 – 4.76
|
<0.001
|
|
|
|
1.05
|
-0.25 – 2.35
|
0.112
|
|
|
|
0.03
|
-0.75 – 0.80
|
0.948
|
|
|
|
|
Observations
|
372
|
100
|
372
|
100
|
372
|
100
|
372
|
100
|
|
R2 / R2 adjusted
|
0.189 / 0.185
|
0.002 / -0.008
|
0.078 / 0.073
|
0.009 / -0.001
|
0.013 / 0.007
|
0.003 / -0.007
|
0.002 / -0.003
|
0.002 / -0.008
|
#weight birth-2 yrs old
par(mfrow=c(2,2))
plot(grim_weight_b_2_f)

par(mfrow=c(2,2))
plot(grim_weight_b_2_m)

par(mfrow=c(2,2))
plot(pheno_weight_b_2_f)

par(mfrow=c(2,2))
plot(pheno_weight_b_2_m)

par(mfrow=c(2,2))
plot(han_weight_b_2_f)

par(mfrow=c(2,2))
plot(han_weight_b_2_m)

2 to 8 years old
#wfaz inf12 minimal models
grim_weight_83_91_f<-lm(AgeAccelGrim ~ wfa_diff_inf12_91 +
wfa_diff_birth_inf12+
was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_weight_83_91_m<-lm(AgeAccelGrim ~ wfa_diff_inf12_91+
wfa_diff_birth_inf12,
subset(growth_clocks_data, sex == "1"))
pheno_weight_83_91_f <-update(grim_weight_83_91_f, AgeAccelPheno ~ .)
pheno_weight_83_91_m <-update(grim_weight_83_91_m, AgeAccelPheno ~ .)
han_weight_83_91_f <-update(grim_weight_83_91_f, EEAA ~ .)
han_weight_83_91_m <-update(grim_weight_83_91_m, EEAA ~ .)
horv_weight_83_91_f <-update(grim_weight_83_91_f, IEAA ~ .)
horv_weight_83_91_m <-update(grim_weight_83_91_m, IEAA ~ .)
sjPlot::tab_model(grim_weight_83_91_f, grim_weight_83_91_m,
pheno_weight_83_91_f, pheno_weight_83_91_m,
han_weight_83_91_f, han_weight_83_91_m,
horv_weight_83_91_f, horv_weight_83_91_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-0.92
|
-1.68 – -0.16
|
0.018
|
1.02
|
-0.91 – 2.96
|
0.296
|
0.13
|
-1.46 – 1.71
|
0.875
|
0.37
|
-3.05 – 3.80
|
0.829
|
0.53
|
-1.12 – 2.18
|
0.532
|
1.83
|
-1.59 – 5.26
|
0.290
|
0.46
|
-0.53 – 1.45
|
0.361
|
-0.41
|
-2.91 – 2.10
|
0.748
|
|
wfa_diff_inf12_91
|
0.20
|
-0.12 – 0.52
|
0.219
|
0.58
|
-0.26 – 1.42
|
0.175
|
0.28
|
-0.39 – 0.95
|
0.412
|
-0.38
|
-1.88 – 1.11
|
0.612
|
0.41
|
-0.29 – 1.11
|
0.247
|
0.49
|
-1.00 – 1.98
|
0.520
|
-0.13
|
-0.55 – 0.29
|
0.537
|
0.29
|
-0.80 – 1.38
|
0.594
|
|
wfa_diff_birth_inf12
|
0.04
|
-0.26 – 0.33
|
0.803
|
0.24
|
-0.44 – 0.93
|
0.480
|
-0.13
|
-0.74 – 0.48
|
0.671
|
-0.63
|
-1.84 – 0.59
|
0.309
|
-0.33
|
-0.97 – 0.30
|
0.303
|
-0.24
|
-1.45 – 0.98
|
0.699
|
-0.22
|
-0.60 – 0.16
|
0.258
|
0.23
|
-0.66 – 1.12
|
0.607
|
|
was_preg_no_na [Yes]
|
2.78
|
2.18 – 3.37
|
<0.001
|
|
|
|
3.47
|
2.23 – 4.71
|
<0.001
|
|
|
|
0.98
|
-0.31 – 2.27
|
0.136
|
|
|
|
0.02
|
-0.75 – 0.80
|
0.954
|
|
|
|
|
Observations
|
370
|
100
|
370
|
100
|
370
|
100
|
370
|
100
|
|
R2 / R2 adjusted
|
0.192 / 0.185
|
0.021 / 0.000
|
0.079 / 0.072
|
0.011 / -0.009
|
0.017 / 0.009
|
0.007 / -0.013
|
0.004 / -0.005
|
0.005 / -0.016
|
#weight 83-91 yrs old
par(mfrow=c(2,2))
plot(grim_weight_83_91_f)

par(mfrow=c(2,2))
plot(grim_weight_83_91_m)

par(mfrow=c(2,2))
plot(pheno_weight_83_91_f)

par(mfrow=c(2,2))
plot(pheno_weight_83_91_m)

par(mfrow=c(2,2))
plot(han_weight_83_91_f)

par(mfrow=c(2,2))
plot(han_weight_83_91_m)

modeling hfaz (interactions)
2 to 8 years old
#hfaz 2years old model
grim_height_83_91_intxn_f<-lm(AgeAccelGrim ~ hfa_diff_inf12_91 *
hfa_diff_birth_inf12 +
was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_height_83_91_intxn_m<-lm(AgeAccelGrim ~ hfa_diff_inf12_91 *
hfa_diff_birth_inf12, subset(growth_clocks_data, sex == "1"))
pheno_height_83_91_intxn_f <-update(grim_height_83_91_intxn_f, AgeAccelPheno ~ .)
pheno_height_83_91_intxn_m <-update(grim_height_83_91_intxn_m, AgeAccelPheno ~ .-was_preg_no_na)
han_height_83_91_intxn_f <-update(grim_height_83_91_intxn_f, EEAA ~ .)
han_height_83_91_intxn_m <-update(grim_height_83_91_intxn_m, EEAA ~ .-was_preg_no_na)
horv_height_83_91_intxn_f <-update(grim_height_83_91_intxn_f, IEAA ~ .)
horv_height_83_91_intxn_m <-update(grim_height_83_91_intxn_m, IEAA ~ .-was_preg_no_na)
sjPlot::tab_model(grim_height_83_91_intxn_f, grim_height_83_91_intxn_m,
pheno_height_83_91_intxn_f, pheno_height_83_91_intxn_m,
han_height_83_91_intxn_f, han_height_83_91_intxn_m,
horv_height_83_91_intxn_f, horv_height_83_91_intxn_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-0.92
|
-1.41 – -0.43
|
<0.001
|
1.75
|
0.71 – 2.80
|
0.001
|
-0.18
|
-1.20 – 0.84
|
0.730
|
-1.81
|
-3.65 – 0.02
|
0.052
|
-0.11
|
-1.17 – 0.96
|
0.844
|
0.60
|
-1.21 – 2.41
|
0.512
|
-0.04
|
-0.67 – 0.60
|
0.904
|
1.03
|
-0.31 – 2.38
|
0.131
|
|
hfa_diff_inf12_91
|
0.25
|
-0.27 – 0.76
|
0.344
|
1.72
|
0.50 – 2.93
|
0.006
|
0.02
|
-1.06 – 1.09
|
0.975
|
1.73
|
-0.40 – 3.86
|
0.110
|
0.24
|
-0.88 – 1.36
|
0.671
|
2.60
|
0.50 – 4.71
|
0.016
|
-0.03
|
-0.70 – 0.63
|
0.919
|
1.73
|
0.17 – 3.30
|
0.031
|
|
hfa_diff_birth_inf12
|
-0.01
|
-0.25 – 0.23
|
0.926
|
0.22
|
-0.33 – 0.76
|
0.428
|
0.08
|
-0.42 – 0.58
|
0.756
|
0.05
|
-0.90 – 1.01
|
0.912
|
0.25
|
-0.26 – 0.77
|
0.335
|
0.14
|
-0.81 – 1.08
|
0.771
|
-0.07
|
-0.38 – 0.24
|
0.673
|
0.51
|
-0.20 – 1.21
|
0.156
|
|
was_preg_no_na [Yes]
|
2.76
|
2.17 – 3.35
|
<0.001
|
|
|
|
3.42
|
2.18 – 4.66
|
<0.001
|
|
|
|
0.86
|
-0.43 – 2.15
|
0.192
|
|
|
|
0.01
|
-0.76 – 0.78
|
0.974
|
|
|
|
hfa_diff_inf12_91 * hfa_diff_birth_inf12
|
0.04
|
-0.16 – 0.24
|
0.680
|
0.43
|
-0.08 – 0.94
|
0.100
|
-0.07
|
-0.49 – 0.34
|
0.723
|
-0.10
|
-1.00 – 0.79
|
0.819
|
-0.13
|
-0.55 – 0.30
|
0.566
|
0.22
|
-0.66 – 1.11
|
0.618
|
0.12
|
-0.13 – 0.38
|
0.339
|
0.52
|
-0.13 – 1.18
|
0.117
|
|
Observations
|
370
|
100
|
370
|
100
|
370
|
100
|
370
|
100
|
|
R2 / R2 adjusted
|
0.192 / 0.183
|
0.087 / 0.059
|
0.077 / 0.067
|
0.095 / 0.067
|
0.014 / 0.003
|
0.112 / 0.085
|
0.012 / 0.001
|
0.080 / 0.051
|
#height 83-91 interaction models
par(mfrow=c(2,2))
plot(grim_height_83_91_intxn_f)

par(mfrow=c(2,2))
plot(grim_height_83_91_intxn_m)

par(mfrow=c(2,2))
plot(pheno_height_83_91_intxn_f)

par(mfrow=c(2,2))
plot(pheno_height_83_91_intxn_m)

par(mfrow=c(2,2))
plot(han_height_83_91_intxn_f)

par(mfrow=c(2,2))
plot(han_height_83_91_intxn_m)

8 to 19 years old
#hfaz_91 minimal models
grim_height_91_02_intxn_f<-lm(AgeAccelGrim ~ hfa_diff_91_02 *
hfa_diff_birth_inf12 +
was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_height_91_02_intxn_m<-lm(AgeAccelGrim ~ hfa_diff_91_02 *
hfa_diff_birth_inf12, subset(growth_clocks_data, sex == "1"))
pheno_height_91_02_intxn_f <-update(grim_height_91_02_intxn_f, AgeAccelPheno ~ .)
pheno_height_91_02_intxn_m <-update(grim_height_91_02_intxn_m, AgeAccelPheno ~ .)
han_height_91_02_intxn_f <-update(grim_height_91_02_intxn_f, EEAA ~ .)
han_height_91_02_intxn_m <-update(grim_height_91_02_intxn_m, EEAA ~ .)
horv_height_91_02_intxn_f <-update(grim_height_91_02_intxn_f, IEAA ~ .)
horv_height_91_02_intxn_m <-update(grim_height_91_02_intxn_m, IEAA ~ .)
sjPlot::tab_model(grim_height_91_02_intxn_f, grim_height_91_02_intxn_m,
pheno_height_91_02_intxn_f, pheno_height_91_02_intxn_m,
han_height_91_02_intxn_f, han_height_91_02_intxn_m,
horv_height_91_02_intxn_f, horv_height_91_02_intxn_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-0.89
|
-1.35 – -0.42
|
<0.001
|
2.08
|
1.00 – 3.16
|
<0.001
|
-0.22
|
-1.20 – 0.76
|
0.659
|
-1.57
|
-3.46 – 0.31
|
0.101
|
-0.39
|
-1.41 – 0.63
|
0.455
|
0.90
|
-1.00 – 2.80
|
0.349
|
0.19
|
-0.41 – 0.79
|
0.538
|
1.67
|
0.31 – 3.02
|
0.017
|
|
hfa_diff_91_02
|
-0.64
|
-1.34 – 0.06
|
0.071
|
-1.99
|
-4.12 – 0.15
|
0.068
|
-0.89
|
-2.35 – 0.58
|
0.233
|
-4.16
|
-7.90 – -0.43
|
0.029
|
-0.26
|
-1.79 – 1.27
|
0.737
|
-3.39
|
-7.14 – 0.36
|
0.076
|
-0.19
|
-1.09 – 0.71
|
0.676
|
-3.24
|
-5.92 – -0.55
|
0.019
|
|
hfa_diff_birth_inf12
|
-0.04
|
-0.24 – 0.17
|
0.740
|
0.24
|
-0.28 – 0.75
|
0.359
|
0.01
|
-0.43 – 0.44
|
0.966
|
-0.26
|
-1.16 – 0.64
|
0.571
|
-0.07
|
-0.53 – 0.38
|
0.754
|
0.06
|
-0.85 – 0.96
|
0.903
|
0.21
|
-0.06 – 0.47
|
0.132
|
0.84
|
0.19 – 1.48
|
0.012
|
|
was_preg_no_na [Yes]
|
2.70
|
2.11 – 3.30
|
<0.001
|
|
|
|
3.43
|
2.17 – 4.68
|
<0.001
|
|
|
|
0.88
|
-0.43 – 2.19
|
0.188
|
|
|
|
0.05
|
-0.72 – 0.82
|
0.901
|
|
|
|
hfa_diff_91_02 * hfa_diff_birth_inf12
|
-0.15
|
-0.44 – 0.14
|
0.304
|
-0.60
|
-1.63 – 0.43
|
0.248
|
-0.24
|
-0.85 – 0.36
|
0.432
|
-1.57
|
-3.37 – 0.23
|
0.087
|
0.12
|
-0.51 – 0.75
|
0.709
|
-1.87
|
-3.68 – -0.07
|
0.042
|
-0.23
|
-0.60 – 0.15
|
0.233
|
-1.50
|
-2.79 – -0.21
|
0.024
|
|
Observations
|
364
|
100
|
364
|
100
|
364
|
100
|
364
|
100
|
|
R2 / R2 adjusted
|
0.196 / 0.187
|
0.047 / 0.017
|
0.082 / 0.072
|
0.065 / 0.036
|
0.011 / -0.001
|
0.049 / 0.020
|
0.010 / -0.001
|
0.090 / 0.061
|
Diagnostics
#height 91-02 interaction models
par(mfrow=c(2,2))
plot(grim_height_91_02_intxn_f)

par(mfrow=c(2,2))
plot(grim_height_91_02_intxn_m)

par(mfrow=c(2,2))
plot(pheno_height_91_02_intxn_f)

par(mfrow=c(2,2))
plot(pheno_height_91_02_intxn_m)

par(mfrow=c(2,2))
plot(han_height_91_02_intxn_f)

par(mfrow=c(2,2))
plot(han_height_91_02_intxn_m)

Visualizations
interact_plot(horv_height_91_02_intxn_m, pred = hfa_diff_91_02, modx = hfa_diff_birth_inf12, plot.points = TRUE)

#
interact_plot(han_height_91_02_intxn_m, pred = hfa_diff_91_02, modx = hfa_diff_birth_inf12, plot.points = TRUE)

modeling wfaz (interactions)
2 to 8 years old
#wfaz inf12 minimal models
grim_weight_83_91_intxn_f<-lm(AgeAccelGrim ~ wfa_diff_inf12_91 * wfa_diff_birth_inf12 + was_preg_no_na, subset(growth_clocks_data, sex == "2"))
grim_weight_83_91_intxn_m<-lm(AgeAccelGrim ~ wfa_diff_inf12_91 * wfa_diff_birth_inf12, subset(growth_clocks_data, sex == "1"))
pheno_weight_83_91_intxn_f <-update(grim_weight_83_91_intxn_f, AgeAccelPheno ~ .)
pheno_weight_83_91_intxn_m <-update(grim_weight_83_91_intxn_m, AgeAccelPheno ~ .)
han_weight_83_91_intxn_f <-update(grim_weight_83_91_intxn_f, EEAA ~ .)
han_weight_83_91_intxn_m <-update(grim_weight_83_91_intxn_m, EEAA ~ .)
horv_weight_83_91_intxn_f <-update(grim_weight_83_91_intxn_f, IEAA ~ .)
horv_weight_83_91_intxn_m <-update(grim_weight_83_91_intxn_m, IEAA ~ .)
sjPlot::tab_model(grim_weight_83_91_intxn_f, grim_weight_83_91_intxn_m,
pheno_weight_83_91_intxn_f, pheno_weight_83_91_intxn_m,
han_weight_83_91_intxn_f, han_weight_83_91_intxn_m,
horv_weight_83_91_intxn_f, horv_weight_83_91_intxn_m)
|
|
Age Accel Grim
|
Age Accel Grim
|
Age Accel Pheno
|
Age Accel Pheno
|
EEAA
|
EEAA
|
IEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-1.07
|
-1.85 – -0.28
|
0.008
|
0.95
|
-1.12 – 3.03
|
0.364
|
-0.26
|
-1.89 – 1.38
|
0.757
|
1.28
|
-2.36 – 4.93
|
0.486
|
0.21
|
-1.49 – 1.91
|
0.807
|
2.63
|
-1.01 – 6.28
|
0.155
|
0.50
|
-0.52 – 1.53
|
0.335
|
-0.60
|
-3.28 – 2.09
|
0.660
|
|
wfa_diff_inf12_91
|
0.66
|
-0.04 – 1.35
|
0.064
|
0.31
|
-2.59 – 3.21
|
0.834
|
1.47
|
0.03 – 2.92
|
0.046
|
3.09
|
-2.01 – 8.18
|
0.232
|
1.39
|
-0.12 – 2.89
|
0.072
|
3.54
|
-1.56 – 8.63
|
0.171
|
-0.26
|
-1.17 – 0.64
|
0.568
|
-0.44
|
-4.19 – 3.32
|
0.818
|
|
wfa_diff_birth_inf12
|
0.07
|
-0.23 – 0.36
|
0.650
|
0.27
|
-0.47 – 1.02
|
0.469
|
-0.05
|
-0.66 – 0.56
|
0.872
|
-0.98
|
-2.29 – 0.33
|
0.140
|
-0.27
|
-0.91 – 0.37
|
0.414
|
-0.55
|
-1.86 – 0.76
|
0.407
|
-0.23
|
-0.61 – 0.16
|
0.245
|
0.31
|
-0.66 – 1.27
|
0.531
|
|
was_preg_no_na [Yes]
|
2.79
|
2.19 – 3.38
|
<0.001
|
|
|
|
3.49
|
2.25 – 4.72
|
<0.001
|
|
|
|
1.00
|
-0.29 – 2.28
|
0.129
|
|
|
|
0.02
|
-0.76 – 0.80
|
0.958
|
|
|
|
wfa_diff_inf12_91 * wfa_diff_birth_inf12
|
-0.19
|
-0.45 – 0.07
|
0.148
|
0.09
|
-0.85 – 1.04
|
0.846
|
-0.51
|
-1.05 – 0.04
|
0.068
|
-1.18
|
-2.84 – 0.48
|
0.161
|
-0.41
|
-0.98 – 0.15
|
0.153
|
-1.04
|
-2.70 – 0.62
|
0.217
|
0.06
|
-0.28 – 0.40
|
0.747
|
0.25
|
-0.97 – 1.47
|
0.687
|
|
Observations
|
370
|
100
|
370
|
100
|
370
|
100
|
370
|
100
|
|
R2 / R2 adjusted
|
0.196 / 0.188
|
0.021 / -0.010
|
0.088 / 0.078
|
0.032 / 0.001
|
0.023 / 0.012
|
0.023 / -0.007
|
0.004 / -0.007
|
0.006 / -0.025
|
Diagnostics
#weight 83-91 interaction models
par(mfrow=c(2,2))
plot(grim_weight_83_91_intxn_f)

par(mfrow=c(2,2))
plot(grim_weight_83_91_intxn_m)

par(mfrow=c(2,2))
plot(pheno_weight_83_91_intxn_f)

par(mfrow=c(2,2))
plot(pheno_weight_83_91_intxn_m)

par(mfrow=c(2,2))
plot(han_weight_83_91_intxn_f)

par(mfrow=c(2,2))
plot(han_weight_83_91_intxn_m)

Looked at another way (weight and height, females only, 83-91)
sjPlot::tab_model(grim_weight_83_91_intxn_f,
pheno_weight_83_91_intxn_f,
han_weight_83_91_intxn_f,
horv_weight_83_91_intxn_f,
grim_height_83_91_intxn_f,
pheno_height_83_91_intxn_f,
han_height_83_91_intxn_f,
horv_height_83_91_intxn_f)
|
|
Age Accel Grim
|
Age Accel Pheno
|
EEAA
|
IEAA
|
Age Accel Grim
|
Age Accel Pheno
|
EEAA
|
IEAA
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
-1.07
|
-1.85 – -0.28
|
0.008
|
-0.26
|
-1.89 – 1.38
|
0.757
|
0.21
|
-1.49 – 1.91
|
0.807
|
0.50
|
-0.52 – 1.53
|
0.335
|
-0.92
|
-1.41 – -0.43
|
<0.001
|
-0.18
|
-1.20 – 0.84
|
0.730
|
-0.11
|
-1.17 – 0.96
|
0.844
|
-0.04
|
-0.67 – 0.60
|
0.904
|
|
wfa_diff_inf12_91
|
0.66
|
-0.04 – 1.35
|
0.064
|
1.47
|
0.03 – 2.92
|
0.046
|
1.39
|
-0.12 – 2.89
|
0.072
|
-0.26
|
-1.17 – 0.64
|
0.568
|
|
|
|
|
|
|
|
|
|
|
|
|
|
wfa_diff_birth_inf12
|
0.07
|
-0.23 – 0.36
|
0.650
|
-0.05
|
-0.66 – 0.56
|
0.872
|
-0.27
|
-0.91 – 0.37
|
0.414
|
-0.23
|
-0.61 – 0.16
|
0.245
|
|
|
|
|
|
|
|
|
|
|
|
|
|
was_preg_no_na [Yes]
|
2.79
|
2.19 – 3.38
|
<0.001
|
3.49
|
2.25 – 4.72
|
<0.001
|
1.00
|
-0.29 – 2.28
|
0.129
|
0.02
|
-0.76 – 0.80
|
0.958
|
2.76
|
2.17 – 3.35
|
<0.001
|
3.42
|
2.18 – 4.66
|
<0.001
|
0.86
|
-0.43 – 2.15
|
0.192
|
0.01
|
-0.76 – 0.78
|
0.974
|
wfa_diff_inf12_91 * wfa_diff_birth_inf12
|
-0.19
|
-0.45 – 0.07
|
0.148
|
-0.51
|
-1.05 – 0.04
|
0.068
|
-0.41
|
-0.98 – 0.15
|
0.153
|
0.06
|
-0.28 – 0.40
|
0.747
|
|
|
|
|
|
|
|
|
|
|
|
|
|
hfa_diff_inf12_91
|
|
|
|
|
|
|
|
|
|
|
|
|
0.25
|
-0.27 – 0.76
|
0.344
|
0.02
|
-1.06 – 1.09
|
0.975
|
0.24
|
-0.88 – 1.36
|
0.671
|
-0.03
|
-0.70 – 0.63
|
0.919
|
|
hfa_diff_birth_inf12
|
|
|
|
|
|
|
|
|
|
|
|
|
-0.01
|
-0.25 – 0.23
|
0.926
|
0.08
|
-0.42 – 0.58
|
0.756
|
0.25
|
-0.26 – 0.77
|
0.335
|
-0.07
|
-0.38 – 0.24
|
0.673
|
hfa_diff_inf12_91 * hfa_diff_birth_inf12
|
|
|
|
|
|
|
|
|
|
|
|
|
0.04
|
-0.16 – 0.24
|
0.680
|
-0.07
|
-0.49 – 0.34
|
0.723
|
-0.13
|
-0.55 – 0.30
|
0.566
|
0.12
|
-0.13 – 0.38
|
0.339
|
|
Observations
|
370
|
370
|
370
|
370
|
370
|
370
|
370
|
370
|
|
R2 / R2 adjusted
|
0.196 / 0.188
|
0.088 / 0.078
|
0.023 / 0.012
|
0.004 / -0.007
|
0.192 / 0.183
|
0.077 / 0.067
|
0.014 / 0.003
|
0.012 / 0.001
|
vif(grim_height_83_91_f)
## hfa_diff_inf12_91 hfa_diff_birth_inf12 was_preg_no_na
## 1.183167 1.179847 1.003111
vif(grim_height_83_91_m)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 1.203573 1.203573
vif(pheno_height_83_91_f)
## hfa_diff_inf12_91 hfa_diff_birth_inf12 was_preg_no_na
## 1.183167 1.179847 1.003111
vif(pheno_height_83_91_m)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 1.203573 1.203573
vif(han_height_83_91_f)
## hfa_diff_inf12_91 hfa_diff_birth_inf12 was_preg_no_na
## 1.183167 1.179847 1.003111
vif(han_height_83_91_m)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 1.203573 1.203573
vif(grim_height_91_02_f)
## hfa_diff_91_02 hfa_diff_birth_inf12 was_preg_no_na
## 1.056932 1.051030 1.006030
vif(grim_height_91_02_m)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 1.020674 1.020674
vif(pheno_height_91_02_f)
## hfa_diff_91_02 hfa_diff_birth_inf12 was_preg_no_na
## 1.056932 1.051030 1.006030
vif(pheno_height_91_02_m)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 1.020674 1.020674
vif(han_height_91_02_f)
## hfa_diff_91_02 hfa_diff_birth_inf12 was_preg_no_na
## 1.056932 1.051030 1.006030
vif(han_height_91_02_m)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 1.020674 1.020674
vif(grim_weight_83_91_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12 was_preg_no_na
## 1.275654 1.284605 1.008342
vif(grim_weight_83_91_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 1.053904 1.053904
vif(pheno_weight_83_91_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12 was_preg_no_na
## 1.275654 1.284605 1.008342
vif(pheno_weight_83_91_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 1.053904 1.053904
vif(han_weight_83_91_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12 was_preg_no_na
## 1.275654 1.284605 1.008342
vif(han_weight_83_91_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 1.053904 1.053904
vif(horv_weight_83_91_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12 was_preg_no_na
## 1.275654 1.284605 1.008342
vif(horv_weight_83_91_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 1.053904 1.053904
vif(grim_height_83_91_intxn_f)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 4.055098 1.561438
## was_preg_no_na hfa_diff_inf12_91:hfa_diff_birth_inf12
## 1.003588 5.066738
vif(grim_height_83_91_intxn_m)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 3.141898 1.471774
## hfa_diff_inf12_91:hfa_diff_birth_inf12
## 3.799584
vif(pheno_height_83_91_intxn_f)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 4.055098 1.561438
## was_preg_no_na hfa_diff_inf12_91:hfa_diff_birth_inf12
## 1.003588 5.066738
vif(pheno_height_83_91_intxn_m)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 3.141898 1.471774
## hfa_diff_inf12_91:hfa_diff_birth_inf12
## 3.799584
vif(han_height_83_91_intxn_f)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 4.055098 1.561438
## was_preg_no_na hfa_diff_inf12_91:hfa_diff_birth_inf12
## 1.003588 5.066738
vif(han_height_83_91_intxn_m)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 3.141898 1.471774
## hfa_diff_inf12_91:hfa_diff_birth_inf12
## 3.799584
vif(horv_height_83_91_intxn_f)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 4.055098 1.561438
## was_preg_no_na hfa_diff_inf12_91:hfa_diff_birth_inf12
## 1.003588 5.066738
vif(horv_height_83_91_intxn_m)
## hfa_diff_inf12_91 hfa_diff_birth_inf12
## 3.141898 1.471774
## hfa_diff_inf12_91:hfa_diff_birth_inf12
## 3.799584
vif(grim_height_91_02_intxn_f)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.111082 1.176695
## was_preg_no_na hfa_diff_91_02:hfa_diff_birth_inf12
## 1.008046 4.442128
vif(grim_height_91_02_intxn_m)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.117563 1.257104
## hfa_diff_91_02:hfa_diff_birth_inf12
## 4.576882
vif(pheno_height_91_02_intxn_f)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.111082 1.176695
## was_preg_no_na hfa_diff_91_02:hfa_diff_birth_inf12
## 1.008046 4.442128
vif(pheno_height_91_02_intxn_m)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.117563 1.257104
## hfa_diff_91_02:hfa_diff_birth_inf12
## 4.576882
vif(han_height_91_02_intxn_f)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.111082 1.176695
## was_preg_no_na hfa_diff_91_02:hfa_diff_birth_inf12
## 1.008046 4.442128
vif(han_height_91_02_intxn_m)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.117563 1.257104
## hfa_diff_91_02:hfa_diff_birth_inf12
## 4.576882
vif(horv_height_91_02_intxn_f)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.111082 1.176695
## was_preg_no_na hfa_diff_91_02:hfa_diff_birth_inf12
## 1.008046 4.442128
vif(horv_height_91_02_intxn_m)
## hfa_diff_91_02 hfa_diff_birth_inf12
## 4.117563 1.257104
## hfa_diff_91_02:hfa_diff_birth_inf12
## 4.576882
vif(grim_weight_83_91_intxn_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 5.939198 1.311235
## was_preg_no_na wfa_diff_inf12_91:wfa_diff_birth_inf12
## 1.008646 5.363499
vif(grim_weight_83_91_intxn_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 12.384711 1.233082
## wfa_diff_inf12_91:wfa_diff_birth_inf12
## 13.154471
vif(pheno_weight_83_91_intxn_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 5.939198 1.311235
## was_preg_no_na wfa_diff_inf12_91:wfa_diff_birth_inf12
## 1.008646 5.363499
vif(pheno_weight_83_91_intxn_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 12.384711 1.233082
## wfa_diff_inf12_91:wfa_diff_birth_inf12
## 13.154471
vif(han_weight_83_91_intxn_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 5.939198 1.311235
## was_preg_no_na wfa_diff_inf12_91:wfa_diff_birth_inf12
## 1.008646 5.363499
vif(han_weight_83_91_intxn_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 12.384711 1.233082
## wfa_diff_inf12_91:wfa_diff_birth_inf12
## 13.154471
vif(horv_weight_83_91_intxn_f)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 5.939198 1.311235
## was_preg_no_na wfa_diff_inf12_91:wfa_diff_birth_inf12
## 1.008646 5.363499
vif(horv_weight_83_91_intxn_m)
## wfa_diff_inf12_91 wfa_diff_birth_inf12
## 12.384711 1.233082
## wfa_diff_inf12_91:wfa_diff_birth_inf12
## 13.154471